

import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= TapasConfig.from_json_file(a__)
			# set absolute/relative position embeddings parameter
			a_						:						Dict 							= reset_position_index_per_cell

			# set remaining parameters of TapasConfig as well as the model based on the task
			if task == "SQA":
						a_						:						str 							= TapasForQuestionAnswering(config=a__)
			elif task == "WTQ":
						# run_task_main.py hparams
						a_						:						Optional[Any] 							= 4
						a_						:						Union[str, Any] 							= True
						# hparam_utils.py hparams
						a_						:						str 							= 0.66_4694
						a_						:						List[Any] 							= 0.20_7951
						a_						:						Optional[int] 							= 0.12_1194
						a_						:						List[Any] 							= True
						a_						:						Tuple 							= True
						a_						:						Any 							= False
						a_						:						List[str] 							= 0.035_2513

						a_						:						Optional[Any] 							= TapasForQuestionAnswering(config=a__)
			elif task == "WIKISQL_SUPERVISED":
						# run_task_main.py hparams
						a_						:						Dict 							= 4
						a_						:						Dict 							= False
						# hparam_utils.py hparams
						a_						:						Any 							= 36.4519
						a_						:						List[Any] 							= 0.90_3421
						a_						:						Union[str, Any] 							= 222.088
						a_						:						List[Any] 							= True
						a_						:						Optional[int] 							= True
						a_						:						str 							= True
						a_						:						Optional[int] 							= 0.76_3141

						a_						:						Any 							= TapasForQuestionAnswering(config=a__)
			elif task == "TABFACT":
						a_						:						List[Any] 							= TapasForSequenceClassification(config=a__)
			elif task == "MLM":
						a_						:						Tuple 							= TapasForMaskedLM(config=a__)
			elif task == "INTERMEDIATE_PRETRAINING":
						a_						:						Any 							= TapasModel(config=a__)
			else:
						raise ValueError(f'''Task {task} not supported.''')

			print(f'''Building PyTorch model from configuration: {config}''')
			# Load weights from tf checkpoint
			load_tf_weights_in_tapas(a__  ,	a__  ,	a__)

			# Save pytorch-model (weights and configuration)
			print(f'''Save PyTorch model to {pytorch_dump_path}''')
			model.save_pretrained(a__)

			# Save tokenizer files
			print(f'''Save tokenizer files to {pytorch_dump_path}''')
			a_						:						Tuple 							= TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt"""  ,	model_max_length=5_1_2)
			tokenizer.save_pretrained(a__)

			print("""Used relative position embeddings:"""  ,	model.config.reset_position_index_per_cell)


if __name__ == "__main__":
	__snake_case				:      Optional[Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
	)
	parser.add_argument(
	    """--reset_position_index_per_cell""",
	    default=False,
	    action="""store_true""",
	    help="""Whether to use relative position embeddings or not. Defaults to True.""",
	)
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--tapas_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained TAPAS model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	__snake_case				:      Tuple   		=			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.task,
	    args.reset_position_index_per_cell,
	    args.tf_checkpoint_path,
	    args.tapas_config_file,
	    args.pytorch_dump_path,
	)





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


